Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/43072
Title: Neural network modelling of a depollution process
Author: Steyer, J.P.
Pelayo-Ortiz, C.
Gonzalez-Alvarez, V.
Bonnet, B.
Bories, A.
Issue Date: 2000
Abstract: In this paper an artificial neural network is developed to model a new depollution process that uses sequential cultures of anaerobic bacteria and yeasts to efficiently remove both carbon and nitrogen from wastewaters. A set of batch experimental runs are used to train and test various neural network topologies. It is shown that the neural network accurately tracks the dynamics of the biological species of the yeast reactor in the process and account for the influence of butyric acid, ammonia and pH on the overall efficiency of purification.
URI: http://hdl.handle.net/20.500.12104/43072
http://www.scopus.com/inward/record.url?eid=2-s2.0-2442430566&partnerID=40&md5=f330e202c44e901a9c44835c070aeaf0
Appears in Collections:Producción científica UdeG

Files in This Item:
There are no files associated with this item.


Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.